Methods and systems for behavior signal generation and processing

ABSTRACT

Instances of claim data are received by an apparatus. Each instance of claim data corresponds to an entity. A claim vector is generated for each instance of claim data. The claim vector is added to a group of claim vectors that all correspond to a same entity. Each group of claim vectors is aggregated to generate an entity vector corresponding to the entity. Based at least in part on the entity vector and (a) an entity profile corresponding to the corresponding entity or (b) an entity cluster with which the entity is associated, at least one behavior signal value is determined for the entity. A behavior signal is amended to include the behavior signal value. The behavior signal comprises at least two behavior signal values. Each of the behavior signal values are associated with a different time period. The behavior signal is provided for display and/or further processing.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/747,991 filed Jan. 21, 2020, which is hereby incorporated herein inits entirety by reference.

TECHNICAL FIELD

Various embodiments relate to the generation and processing of behaviorsignals. For instance, various embodiments generate behavior signalscorresponding to medical provider behaviors.

BACKGROUND

In healthcare systems and their relevant components, members/patientsand providers generate a high volume of claims for insurance companies.From the insurance company point of view, it is very important to trackany abnormalities in the system and, more specifically, any abnormalityin the submitted claims. Such abnormality tracking is important fordetecting any gaming of the system that emerges from policies andclaims. Currently various claim level abnormality detection are in placefor medical cost saving purposes. However, detecting individual abnormalclaims without providing insight to a larger picture is insufficient fordetecting complex gaming behaviors of providers and members in thehealthcare domain. Due to the infeasibility of having label data forthese gaming behaviors, it is not possible to build supervised machinelearning models to detect these patterns and trends from claiminformation/data.

BREIF SUMMARY OF SOME EXAMPLE EMBODIMENTS

Various embodiments provide methods, systems, apparatus, computerprogram products, and/or the like for generating behavior signals forentities. In various embodiments, the entities are members/patients orproviders (e.g., physicians, physician groups, and/or other healthcareproviders). In various embodiments, the entities further includetreatments, policies, states (e.g., government organizations and/orgeographic regions), and/or the like. In various embodiments, thebehavior signals indicate changes in entity cluster behavior, changes inentity behavior with respect to a peer cluster, changes in entitybehavior with respect to the previous behavior of the entity, and/or thelike. Various embodiments provide a fully automated end-to-end pipelinewhich is able to generate multiple behavior signals from raw claiminformation/data. For example, various embodiments generate one or morebehavior signals without the use of manually labeled data. In variousembodiments, the behavior signals are used to identify patterns inentity behavior, anomalous behavior, predict future entity behavior,and/or the like.

In various embodiments, instances of claim data/information are receivedand elements of claim features are extracted therefrom. Element vectorsare generated for each extracted claim feature (e.g., via a vectorembedding). For example, the categorical features of an instance ofclaim information/data may be embedded into an array of vectors using amachine-learning-trained Categorical to Vector model. The Categorical toVector model may be a self-supervised model which learns the embeddingsof elements of features of instances of claim information/data byconsidering the overall context of each claim in place of a traininglabel. The numerical elements of the features of the instances of claiminformation/data may be normalized and/or scaled and combined with thearray of vectors generated by Categorical to Vector model. This arraymay then be provided as input to an auto-encoder model (e.g., trainedvia a machine learning algorithm) to determine an anomaly score for eachclaim by calculating the re-construction error for the array of vectors(e.g., using the decoder portion of the auto-encoder) and to aggregatethe array of vectors into a claim vector (e.g., using the encoderportion of the auto-encoder). For instance, the array of vectorsdetermined based at least in part on the categorical and numericalelements of the features of the instances of claim information/data maybe aggregated to generate a claim vector for the claim.

The claim vectors are grouped based at least in part on correspondingentities (e.g., a member/patient identified in the instance of claiminformation/data and/or a provider identified in the instance of claiminformation/data) and the group of claim vectors are aggregated togenerate an entity vector. Entities may be clustered into entityclusters (e.g., member peer cluster, provider peer cluster, providerspecialty cluster, and/or the like). Behavior signal values may begenerated by comparing the entity vector corresponding to an entity witha cluster vector corresponding to an entity cluster with which theentity is affiliated and/or associated. In an example embodiment, abehavior signal value may be generated by comparing the entity vectorcorresponding to an entity to a previous entity vector corresponding tothe entity.

In various embodiments, an entity profile corresponding to each entityis stored by an analysis computing entity. The entity profilecorresponding to an entity may include an entity identifier (e.g.,member identifier, provider identifier) configured to identify theentity, entity clusters with which the entity is affiliated and/orassociated, behavior signals, and/or the like. For example, a behaviorsignal may comprise a plurality of behavior signal values that are timeordered. For example, each behavior signal value may be associated witha time. For instance, in an example embodiment, the instances of claiminformation/data for a day, week, month, quarter, and/or the like may beanalyzed to generate a corresponding behavior signal value. Thetime-ordered sequence of a behavior signal values is a behavior signalthat illustrates how an entities behavior has evolved over time withrespect to an entity cluster that the entity is associated with, withrespect the entity's own behavior, and/or the like. When a new behaviorsignal value is determined for the entity, the entity profile may beupdated to include the new behavior signal value and to extend thecorresponding behavior signal. The behavior signals for one or moreentities may be provided for user review via an interactive userinterface of a user computing entity, processed to identify patterns inthe entity behavior and/or patterns in entity behavior corresponding toan entity cluster, used to predict future entity and/or entity clusterbehavior, processed to generate entity suggestions, and/or the like.

According to one aspect, a method for tracking entity behavior overtime. In an example embodiment, the method comprises receiving, by ananalysis computing entity, one or more instances of claim data. Eachinstance of claim data corresponds to an entity and comprising one ormore features. The method further comprises generating, by the analysiscomputing entity, a claim vector for each instance of claim data basedat least in part on the one or more features of the instance of claimdata; adding, by the analysis computing entity, the claim vector to agroup of claim vectors, wherein each claim vector within the group ofclaim vectors corresponds to a same corresponding entity; andaggregating, by the analysis computing entity, each group of claimvectors to generate an entity vector corresponding to the correspondingentity. The method further comprises based at least in part on theentity vector and at least one of (a) an entity profile corresponding tothe corresponding entity or (b) an entity cluster with which thecorresponding entity is associated, determining, by the analysiscomputing entity, at least one behavior signal value for thecorresponding entity; amending, by the analysis computing entity, abehavior signal to include the at least one behavior signal value, thebehavior signal comprising at least two behavior signal values, each ofthe at least two behavior signal values associated with a time period;and providing, by the analysis computing entity, the behavior signal forat least one of (a) display via an interactive user interface of a usercomputing entity or (b) further processing for pattern identificationand/or behavior prediction.

According to another aspect, an apparatus is provided. In an exampleembodiment, the apparatus comprises at least one processor, at least onecommunications interface, and at least one memory including computerprogram code. The computer program code comprises executableinstructions. The at least one memory and computer program code areconfigured to, with the processor, cause the apparatus to at leastreceive one or more instances of claim data, each instance of claim datacorresponding to an entity and comprising one or more features; generatea claim vector for each instance of claim data based at least in part onthe one or more features of the instance of claim data; add the claimvector to a group of claim vectors, wherein each claim vector within thegroup of claim vectors corresponds to a same corresponding entity;aggregate each group of claim vectors to generate an entity vectorcorresponding to the corresponding entity; based at least in part on theentity vector and at least one of (a) an entity profile corresponding tothe corresponding entity or (b) an entity cluster with which thecorresponding entity is associated, determine at least one behaviorsignal value for the corresponding entity; amend a behavior signal toinclude the at least one behavior signal value, the behavior signalcomprising at least two behavior signal values, each of the at least twobehavior signal values associated with a time period; and provide thebehavior signal for at least one of (a) display via an interactive userinterface of a user computing entity or (b) further processing forpattern identification and/or behavior prediction.

According to yet another aspect, a computer program product is provided.In an example embodiment, the computer program product comprises atleast one non-transitory computer-readable storage medium havingcomputer-executable program code portions stored therein. Thecomputer-executable program code portions comprise program codeinstructions. The computer program code instructions, when executed by aprocessor of a computing entity, are configured to cause the computingentity to receive one or more instances of claim data, each instance ofclaim data corresponding to an entity and comprising one or morefeatures; generate a claim vector for each instance of claim data basedat least in part on the one or more features of the instance of claimdata; add the claim vector to a group of claim vectors, wherein eachclaim vector within the group of claim vectors corresponds to a samecorresponding entity; aggregate each group of claim vectors to generatean entity vector corresponding to the corresponding entity; based atleast in part on the entity vector and at least one of (a) an entityprofile corresponding to the corresponding entity or (b) an entitycluster with which the corresponding entity is associated, determine atleast one behavior signal value for the corresponding entity; amend abehavior signal to include the at least one behavior signal value, thebehavior signal comprising at least two behavior signal values, each ofthe at least two behavior signal values associated with a time period;and provide the behavior signal for at least one of (a) display via aninteractive user interface of a user computing entity or (b) furtherprocessing for pattern identification and/or behavior prediction.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 is a diagram of a system that can be used in conjunction withvarious embodiments of the present invention;

FIG. 2A is a schematic of an analysis computing entity in accordancewith certain embodiments of the present invention;

FIG. 2B is a schematic representation of a memory media storing aplurality of data assets;

FIG. 3 is a schematic of a user computing entity in accordance withcertain embodiments of the present invention;

FIG. 4 provides a flowchart illustrating various processes, procedures,and/or operations performed to generate an entity vector from claiminformation/data, in accordance with certain embodiments of the presentinvention;

FIG. 5A illustrates an example of aggregating claim feature vectors togenerate a claim vector, in accordance with certain embodiments of thepresent invention;

FIG. 5B illustrates an example of aggregating claim vectors to generatean entity vector, in accordance with certain embodiments of the presentinvention;

FIG. 6 provides a flowchart illustrating various processes, procedures,and/or operations performed to generate a behavior signal value and/oranalyze a behavior signal, in accordance with certain embodiments of thepresent invention;

FIG. 7 is a schematic diagram illustrating the generation of entityclusters from entity vectors, in accordance with certain embodiments ofthe present invention;

FIG. 8A is a schematic diagram showing the evolution of behavior of anentity cluster and a particular entity with respect to the entitycluster over time, in accordance with certain embodiments of the presentinvention;

FIG. 8B illustrates a behavior signal, in accordance with certainembodiments of the present invention;

FIG. 9 illustrates an example dashboard provided as an interactive userinterface via a user interface of a user computing entity, in an exampleembodiment; and

FIG. 10 illustrates an example prediction dashboard view provided viathe interactive user interface, in an example embodiment.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” (also designated as “/”) is usedherein in both the alternative and conjunctive sense, unless otherwiseindicated. The terms “illustrative” and “exemplary” are used to beexamples with no indication of quality level. Like numbers refer to likeelements throughout.

I. Computer Program Products, Methods, and Computing Entities

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. Such computer program products may include one or moresoftware components including, for example, software objects, methods,data structures, and/or the like. A software component may be coded inany of a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastorage construct. Software components of a similar type or functionallyrelated may be stored together such as, for example, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DIMM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of a data structure, apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisescombination of computer program products and hardware performing certainsteps or operations.

Embodiments of the present invention are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For instance, retrieval, loading, and execution of code may be performedsequentially such that one instruction is retrieved, loaded, andexecuted at a time. In some exemplary embodiments, retrieval, loading,and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically-configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

II. Exemplary System Architecture

FIG. 1 provides an illustration of a system 100 that can be used inconjunction with various embodiments of the present invention. As shownin FIG. 1, the system 100 may comprise one or more analysis computingentities 65, one or more claim computing entities 50, one or more usercomputing entities 30, one or more networks 135, and/or the like. Invarious embodiments, the one or more user computing entities 30 compriseprovider computing entities, patient/member computing entities, and/orinsurance company affiliate computing entities (e.g., computing entitiesoperated by employees of an insurance company). Each of the componentsof the system may be in electronic communication with, for example, oneanother over the same or different wireless or wired networks 135including, for example, a wired or wireless Personal Area Network (PAN),Local Area Network (LAN), Metropolitan Area Network (MAN), Wide AreaNetwork (WAN), and/or the like. Additionally, while FIG. 1 illustratecertain system entities as separate, standalone entities, the variousembodiments are not limited to this particular architecture.

a. Exemplary Analysis Computing Entity

FIG. 2A provides a schematic of an analysis computing entity 65according to one embodiment of the present invention. In variousembodiments, the analysis computing entity 65 executes one or moreprogram modules, application program code, sets of computer executableinstructions, and/or the like to generate behavior signal values for oneor more entities, process behavior signals for one or more entities,and/or the like. In general, the terms computing entity, entity, device,system, and/or similar words used herein interchangeably may refer to,for instance, one or more computers, computing entities, desktopcomputers, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, items/devices, terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Such functions, operations, and/or processes may include, forexample, transmitting, receiving, operating on, processing, displaying,storing, determining, creating/generating, monitoring, evaluating,comparing, and/or similar terms used herein interchangeably. In oneembodiment, these functions, operations, and/or processes can beperformed on data, content, information, and/or similar terms usedherein interchangeably.

As indicated, in one embodiment, the analysis computing entity 65 mayalso include one or more network and/or communications interfaces 208for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. For instance, theanalysis computing entity 65 may communicate with other computingentities, one or more user computing entities 30, and/or the like.

As shown in FIG. 2A, in one embodiment, the analysis computing entity 65may include or be in communication with one or more processing elements205 (also referred to as processors, processing circuitry, and/orsimilar terms used herein interchangeably) that communicate with otherelements within the analysis computing entity 65 via a bus, forinstance, or network connection. As will be understood, the processingelement 205 may be embodied in a number of different ways. For example,the processing element 205 may be embodied as one or more complexprogrammable logic devices (CPLDs), microprocessors, multi-coreprocessors, coprocessing entities, application-specific instruction-setprocessors (ASIPs), and/or controllers. Further, the processing element205 may be embodied as one or more other processing devices orcircuitry. The term circuitry may refer to an entirely hardwareembodiment or a combination of hardware and computer program products.Thus, the processing element 205 may be embodied as integrated circuits,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), programmable logic arrays (PLAs), hardwareaccelerators, other circuitry, and/or the like. As will therefore beunderstood, the processing element 205 may be configured for aparticular use or configured to execute instructions stored in volatileor non-volatile media or otherwise accessible to the processing element205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present invention when configured accordingly.

In one embodiment, the analysis computing entity 65 may further includeor be in communication with non-volatile media (also referred to asnon-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In one embodiment, thenon-volatile storage or memory may include one or more non-volatilestorage or memory media 206 as described above, such as hard disks, ROM,PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks,CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, and/or the like. Aswill be recognized, the non-volatile storage or memory media may storedatabases, metadata repositories database instances, database managementsystem entities, data, applications, programs, program modules, scripts,source code, object code, byte code, compiled code, interpreted code,machine code, executable instructions, and/or the like. For instance, asshown in FIG. 2B, the memory media 206 may store computer executablecode that, when executed by the processing element 205, causes theoperation of an embedding module 220, clustering module 222, signalgeneration module 224, signal processing module 226, and/or the like,which are described in detail elsewhere herein. Though described asmodules herein, the embedding module 220, clustering module 222, signalgeneration module 224, and/or signal processing module 226 may beembodied by various forms of computer-executable instructions,program/application code, and/or the like, in various embodiments. Theterm database, database instance, database management system entity,and/or similar terms used herein interchangeably and in a general senseto refer to a structured or unstructured collection of information/datathat is stored in a computer-readable storage medium.

Memory media 206 (e.g., metadata repository) may also be embodied as adata storage device or devices, as a separate database server orservers, or as a combination of data storage devices and separatedatabase servers. Further, in some embodiments, memory media 206 may beembodied as a distributed repository such that some of the storedinformation/data is stored centrally in a location within the system andother information/data is stored in one or more remote locations.Alternatively, in some embodiments, the distributed repository may bedistributed over a plurality of remote storage locations only. Anexample of the embodiments contemplated herein would include a clouddata storage system maintained by a third party provider and where someor all of the information/data required for the operation of the systemmay be stored. As a person of ordinary skill in the art would recognize,the information/data required for the operation of the system may alsobe partially stored in the cloud data storage system and partiallystored in a locally maintained data storage system.

Memory media 206 (e.g., metadata repository) may includeinformation/data accessed and stored by the system to facilitate theoperations of the system. More specifically, memory media 206 mayencompass one or more data stores configured to store information/datausable in certain embodiments. For example, as shown in FIG. 2B,metadata for data assets may be stored in metadata repositoriesencompassed within the memory media 206. The metadata for the dataassets in the metadata data stores, metadata repositories, and similarwords used herein interchangeably may comprise claims information/data211, entity profiles (e.g., member information/data 212, providerinformation/data 213, and/or the like), treatment information/data 214,and/or various other types of information/data. In an exampleembodiment, the memory media 206 may store patient/member datarepositories, provider data repositories, care standard datarepositories, and/or the like. As will be recognized, metadatarepositories are inventories data assets in an organization'senvironment.

In one embodiment, the analysis computing entity 65 may further includeor be in communication with volatile media (also referred to as volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). In one embodiment, the volatile storage ormemory may also include one or more volatile storage or memory media 207as described above, such as RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM,DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cachememory, register memory, and/or the like. As will be recognized, thevolatile storage or memory media may be used to store at least portionsof the databases, database instances, database management systementities, data, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like being executed by, forinstance, the processing element 205. Thus, the databases, databaseinstances, database management system entities, data, applications,programs, program modules, scripts, source code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like may be used to control certain aspects of the operationof the analysis computing entity 65 with the assistance of theprocessing element 205 and operating system.

As indicated, in one embodiment, the analysis computing entity 65 mayalso include one or more network and/or communications interfaces 208for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. For instance, theanalysis computing entity 65 may communicate with computing entities orcommunication interfaces of other computing entities 65, user computingentities 30, and/or the like. In this regard, the analysis computingentity 65 may access various data assets.

As indicated, in one embodiment, the analysis computing entity 65 mayalso include one or more network and/or communications interfaces 208for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired data transmission protocol, such as fiberdistributed data interface (FDDI), digital subscriber line (DSL),Ethernet, asynchronous transfer mode (ATM), frame relay, data over cableservice interface specification (DOCSIS), or any other wiredtransmission protocol. Similarly, the analysis computing entity 65 maybe configured to communicate via wireless external communicationnetworks using any of a variety of protocols, such as general packetradio service (GPRS), Universal Mobile Telecommunications System (UMTS),Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1X (1xRTT),Wideband Code Division Multiple Access (WCDMA), Global System for MobileCommunications (GSM), Enhanced Data rates for GSM Evolution (EDGE), TimeDivision-Synchronous Code Division Multiple Access (TD-SCDMA), Long TermEvolution (LTE), Evolved Universal Terrestrial Radio Access Network(E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access(HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi),Wi-Fi Direct, 802.16 (WiMAX), ultra-wideband (UWB), infrared (IR)protocols, near field communication (NFC) protocols, Wibree, Bluetoothprotocols, wireless universal serial bus (USB) protocols, and/or anyother wireless protocol. The analysis computing entity 65 may use suchprotocols and standards to communicate using Border Gateway Protocol(BGP), Dynamic Host Configuration Protocol (DHCP), Domain Name System(DNS), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP),HTTP over TLS/SSL/Secure, Internet Message Access Protocol (IMAP),Network Time Protocol (NTP), Simple Mail Transfer Protocol (SMTP),Telnet, Transport Layer Security (TLS), Secure Sockets Layer (SSL),Internet Protocol (IP), Transmission Control Protocol (TCP), UserDatagram Protocol (UDP), Datagram Congestion Control Protocol (DCCP),Stream Control Transmission Protocol (SCTP), HyperText Markup Language(HTML), and/or the like.

As will be appreciated, one or more of the central computing entity'scomponents may be located remotely from other analysis computing entity65 components, such as in a distributed system. Furthermore, one or moreof the components may be aggregated and additional components performingfunctions described herein may be included in the analysis computingentity 65. Thus, the analysis computing entity 65 can be adapted toaccommodate a variety of needs and circumstances.

b. Exemplary User Computing Entity

FIG. 3 provides an illustrative schematic representative of usercomputing entity 30 that can be used in conjunction with embodiments ofthe present invention. In various embodiments, a user computing entity30 may be a provider computing entity operated by and/or on behalf of aprovider. In various embodiments, a provider is a healthcare provider;clinic; hospital; healthcare provider group; administrative and/orclinical staff associated with a healthcare provider, clinic, hospital,healthcare provider group, and/or the like; and/or other provider ofhealthcare services. In various embodiments, a user computing entity 30is a patient/member computing entity. In various embodiments, apatient/member computing entity is operated by and/or on behalf of apatient and/or member of a policy, plan, and/or the like provided by aninsurance company, payor, and/or other entity. In various embodiments, auser computing entity 30 is an insurance company affiliate computingentity. In various embodiments, an insurance company affiliate is anemployee of an insurance company.

As will be recognized, the user computing entity may be operated by anagent and include components and features similar to those described inconjunction with the analysis computing entity 65. Further, as shown inFIG. 3, the user computing entity may include additional components andfeatures. For example, the user computing entity 30 can include anantenna 312, a transmitter 304 (e.g., radio), a receiver 306 (e.g.,radio), and a processing element 308 that provides signals to andreceives signals from the transmitter 304 and receiver 306,respectively. The signals provided to and received from the transmitter304 and the receiver 306, respectively, may include signalinginformation/data in accordance with an air interface standard ofapplicable wireless systems to communicate with various entities, suchas an analysis computing entity 65, another user computing entity 30,and/or the like. In this regard, the user computing entity 30 may becapable of operating with one or more air interface standards,communication protocols, modulation types, and access types. Moreparticularly, the user computing entity 30 may operate in accordancewith any of a number of wireless communication standards and protocols.In a particular embodiment, the user computing entity 30 may operate inaccordance with multiple wireless communication standards and protocols,such as GPRS, UMTS, CDMA2000, 1xRTT, WCDMA, TD-SCDMA, LTE, E-UTRAN,EVDO, HSPA, HSDPA, Wi-Fi, WiMAX, UWB, IR protocols, Bluetooth protocols,USB protocols, and/or any other wireless protocol.

Via these communication standards and protocols, the user computingentity 30 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The user computing entity 30 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the user computing entity 30 may includelocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For instance, the usercomputing entity 30 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, UTC, date, and/orvarious other information/data. In one embodiment, the location modulecan acquire data, sometimes known as ephemeris data, by identifying thenumber of satellites in view and the relative positions of thosesatellites. The satellites may be a variety of different satellites,including LEO satellite systems, DOD satellite systems, the EuropeanUnion Galileo positioning systems, the Chinese Compass navigationsystems, Indian Regional Navigational satellite systems, and/or thelike. Alternatively, the location information/data/data may bedetermined by triangulating the position in connection with a variety ofother systems, including cellular towers, Wi-Fi access points, and/orthe like. Similarly, the user computing entity 30 may include indoorpositioning aspects, such as a location module adapted to acquire, forinstance, latitude, longitude, altitude, geocode, course, direction,heading, speed, time, date, and/or various other information/data. Someof the indoor aspects may use various position or location technologiesincluding RFID tags, indoor beacons or transmitters, Wi-Fi accesspoints, cellular towers, nearby computing devices (e.g., smartphones,laptops) and/or the like. For instance, such technologies may includeiBeacons, Gimbal proximity beacons, BLE transmitters, Near FieldCommunication (NFC) transmitters, and/or the like. These indoorpositioning aspects can be used in a variety of settings to determinethe location of someone or something to within inches or centimeters.

The user computing entity 30 may also comprise a user interfacecomprising one or more user input/output devices/interfaces (e.g., adisplay 316 and/or speaker/speaker driver coupled to a processingelement 308 and a touch screen, keyboard, mouse, and/or microphonecoupled to a processing element 308). For example, the user outputdevice/interface may be configured to provide an application, browser,interactive user interface (IUI), dashboard, webpage, Internetaccessible/online portal, and/or similar words used hereininterchangeably executing on and/or accessible via the user computingentity 30 to cause display or audible presentation of information/dataand for user interaction therewith via one or more user inputdevices/interfaces. The user output interface may be updated dynamicallyfrom communication with the analysis computing entity 65. The user inputdevice/interface can comprise any of a number of devices allowing theuser computing entity 30 to receive information/data, such as a keypad318 (hard or soft), a touch display, voice/speech or motion interfaces,scanners, readers, or other input device. In embodiments including akeypad 318, the keypad 318 can include (or cause display of) theconventional numeric (0-9) and related keys (#, *), and other keys usedfor operating the user computing entity 30 and may include a full set ofalphabetic keys or set of keys that may be activated to provide a fullset of alphanumeric keys. In addition to providing input, the user inputdevice/interface can be used, for instance, to activate or deactivatecertain functions, such as screen savers and/or sleep modes. Throughsuch inputs the user computing entity 30 can collect information/data,user interaction/input, and/or the like.

The user computing entity 30 can also include volatile storage or memory322 and/or non-volatile storage or memory 324, which can be embeddedand/or may be removable. For example, the non-volatile memory may beROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, and/or thelike. The volatile memory may be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM,SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management system entities, data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like to implement the functions of the user computing entity 30.

c. Claims Computing Entity

In various embodiments, a claims computing entity 50 is a computingentity configured to receive, process, and/or provide claimsinformation/data. In various embodiments, a claims computing entity 50may receive instances of claims information/data provided, for instance,by a user computing entity 30. The claims computing entity 50 mayprocess the instances of claims information/data (e.g., to process thecorresponding claims) and/or may provide (e.g., transmit) instances ofclaims information/data such that an analysis computing entity 65receives the instances of claims information/data. In variousembodiments, a claims computing entity 50 comprises one or morecomponents similar to those described above with respect to the analysiscomputing entity 65 and/or the user computing entity 30. For example,the claims computing entity 50 may comprise one or more processingelements, memories (e.g., volatile and/or non-volatile), communicationinterfaces, user interfaces, and/or the like.

d. Exemplary Networks

In one embodiment, the networks 135 may include, but are not limited to,any one or a combination of different types of suitable communicationsnetworks such as, for instance, cable networks, public networks (e.g.,the Internet), private networks (e.g., frame-relay networks), wirelessnetworks, cellular networks, telephone networks (e.g., a public switchedtelephone network), or any other suitable private and/or publicnetworks. Further, the networks 135 may have any suitable communicationrange associated therewith and may include, for example, global networks(e.g., the Internet), MANs, WANs, LANs, or PANs. In addition, thenetworks 135 may include any type of medium over which network trafficmay be carried including, but not limited to, coaxial cable,twisted-pair wire, optical fiber, a hybrid fiber coaxial (HFC) medium,microwave terrestrial transceivers, radio frequency communicationmediums, satellite communication mediums, or any combination thereof, aswell as a variety of network devices and computing platforms provided bynetwork providers or other entities.

III. Technical Advantages

Various embodiments provide an automated technique for identifyinganomalous entity behavior, predicting future entity behavior,identifying and monitoring entity behavior patterns, generatingsuggestions for improvement of entity behavior, and/or the like. Forinstance, in the healthcare domain, insurance providers process millionsof claims. However, traditional techniques for identifying anomalousclaims fail to identify patterns of entity behavior that are anomalous.Embodiments of the present invention provide a technical solution tothis problem by not only identify anomalous patterns of entity behavior,but also providing predictions of entity behavior, suggestions forimprovement of entity behavior and/or the like in an automated manner.The high volume of claims to be processed requires that such a solutionbe a technical computer-based solution. Thus, various embodimentsprovide technical solutions to technical problems related to identifyingpatterns of entity behavior based at least in part on, for example,claim information/data and enabling improvement of entity behavior andthe healthcare environment based at least in part on the identifiedpatterns.

IV. Exemplary System Operation

As described above, various embodiments are configured to transform andanalyze claim information/data to identify patterns in entity behavior.These identified patterns in entity behavior may be used to monitorentity behavior, identify anomalous patterns of entity behavior,determine suggestions for improvement of entity behavior, determinepredictions of future entity behavior based at least in part on variousexternal factors, and/or the like in an automated fashion. For instance,various embodiments provide an automated technique for receiving,transforming, and analyzing claim information/data to provide monitoringof entity behavior, identification of anomalous patterns of entitybehavior, determination of suggestions for improvement of entitybehavior, determination of predictions of future entity behavior basedat least in part on various external factors, and/or the like.

In various embodiments, a claims computing entity 50 receives claimsfrom various user computing entities 30 (e.g., provider computingentities, user computing entities, and/or the like). Each claim providesan instance of claim information/data. Each instance of claiminformation/data comprises a plurality of features such as entityidentifying information/data, categorical elements, numerical elements,temporal information/data, and/or the like. For example, each instanceof claim information/data comprises entity identifying information/datasuch as a member/patient identifier configured to identify themember/patient corresponding to the claim, a provider identifierconfigured to identify the provider corresponding to the claim, alocation corresponding to the claim (e.g., a location where theservice(s) and/or product(s) identified in the claim were provided tothe patient/member), member demographic information/data (e.g., gender,age/age group, etc.), and/or the like. In various embodiments eachinstance of claim information/data comprises categorical elements. Invarious embodiments, the categorical elements comprise medical codes(e.g., current procedural terminology (CPT) codes and/or the like) suchas one or more diagnosis codes, procedure codes, medication codes,equipment codes, and/or other medical codes that describe the service(s)and/or product(s) provided to the member/patient in one or moretransactions corresponding to the claim. In various embodiments, avector embedding may be used to transform one or more categoricalelements of the claims into vectors in a multi-dimensional space. Forinstance, as described in U.S. application Ser. No. 15/927,188, filedMar. 21, 2018, various medical codes may be transformed into vectors ina multi-dimensional space such that the distance between two vectorsprovides an indication of the similarity and/or relationship between thecorresponding codes. In various embodiments, a machine-learning-basedmodel is used to transform the categorical elements into elementvectors. For example, a plurality of element vectors may be generatedbased at least in part on the categorical elements of an instance ofclaim information/data. In various embodiments, the numerical elementscomprise numerical information/data corresponding to the member/patientand/or the service(s) and/or product(s) provided to the member/patientin one or more transactions corresponding to the claim. Somenon-limiting examples of numerical elements include payment values, amember/patient's age, number of units of a product provided, and/or thelike. In various embodiments, a numerical element may be associated witha categorical element. For instance, an associated categorical elementmay provide context for the numerical value of the numerical element. Invarious embodiments, the numerical elements of an instance of claiminformation/data may be normalized, rescaled, and/or the like (e.g.,based at least in part on the associated categorical elements). In anexample embodiment, the normalized, rescaled, and/or the like numericalelements and the associated categorical elements and/or correspondingelement vectors may be provided to a machine-learning-based model. Forexample, the normalized, rescaled, and/or the like numerical elementsand the associated categorical elements and/or corresponding elementvectors may be provided to an auto-encoder network. Themachine-learning-based model may be configured to determine, calculate,and/or the like a claim vector by aggregating the element vectors (e.g.,based at least in part on the associated numerical elements and/ornormalized, rescaled, and/or the like numerical elements), an anomalyscore for the claim vector, and/or the like. In various embodiments,each instance of claim information/data comprises temporalinformation/data corresponding to the date and/or time at which theservice(s) and/or product(s) corresponding to the claim were provided tothe patient/member.

In various embodiments, a plurality of claim vectors are determinedbased at least in part on a plurality of instances of claiminformation/data. Each claim vector is associated with a provideridentifier and a member/patient identifier. The claim vector isassociated with the corresponding provider and/or member/patient. Forinstance, an entity profile corresponding to the provider (e.g., storedin provider data 213) identified by the provider identifier of the claimand/or the patient/member (e.g., stored in member data 212) identifiedby the member/patient identifier of the claim may be amended to includethe claim vector and/or corresponding metadata (e.g., patient/memberidentifier, provider identifier, temporal information/data, and/or thelike). In various embodiments, the claim vectors associated with anentity and a time period are aggregated to generate an entity vectorcorresponding to the time period. For example, an entity vector may begenerated daily, weekly, biweekly, monthly, and/or the like. Forinstance, if an entity vector corresponds to a time period of the monthof January 2020, all of the claim vectors having temporalinformation/data corresponding to a time in January 2020 (e.g., servicesand/or products were provided by the provider to the member/patient inJanuary 2020) may be aggregated to generate an entity vectorcorresponding to January 2020.

In various embodiments, the element vectors are determined in amulti-dimensional space. In various embodiments, this may be a very highdimensional space (e.g., hundreds of dimensions). In variousembodiments, the claim vectors and/or entity vectors may be transformedand/or mapped into a lower multi-dimensional space. For example, theentity vectors may be defined in a high dimensional embedding space andthe dimensionality of the embedding space may be reduced using a UniformManifold Approximation and Projection (UMAP) algorithm, and/or the like.In various embodiments, the dimension reduction process is configured tomaintain one or more relationships (e.g., distances) between variousclaim and/or entity vectors.

In various embodiments, the entity vectors corresponding to providersmay be analyzed to identify one or more provider clusters. In variousembodiments, provider clusters may be geographic clusters identifiedbased at least in part on the geographic location in which providerspractice (e.g., providers practicing the southeast, providers practicingin the state of Texas, provider practicing in the Chicago metro-area,and/or the like). In various embodiments, provider clusters arespecialty clusters identified based at least in part on providerspecialties (e.g., gastroenterologists, urologists, gynecologists,and/or the like). In various embodiments, provider peer clusters may beidentified based at least in part on provider behavior as indicated bythe entity vectors associated with the providers. For instance, adensity-based clustering method may be used to identify one or moreprovider peer clusters based at least in part on the entity vectorscorresponding to the providers in the (reduced) multi-dimensional space.For example, a hierarchical density-based spatial clustering ofapplications with noise (HDB SCAN) clustering algorithm and/or anotherclustering algorithm may be used to identify provider peer clusters(and/or member peer clusters). Similarly, member/patient clusters may beidentified using a geographic location associated with themember/patient (e.g., associated with the corresponding member profilestored in the member data 212), one or more diagnostic codes associatedwith the member/patient, and/or using a density-based (or other)clustering method based at least in part on the entity vectorscorresponding to the members to identify member peer clusters. Invarious embodiments, treatment clusters may also be identified. In anexample embodiment, the treatment clusters are determined and/oridentified by a model trained only on the medical features of the claimsinformation/data. For example, only the medical codes extracted fromeach instance of claim information/data may be used to generatemulti-dimensional vectors which may the ben clustered for the sake ofidentifying treatment clusters. Treatment cluster identifiers may beassigned to the treatment clusters and these treatment clusteridentifiers may then be used to primary model. In various embodiments,cluster identifiers configured to identify the clusters an entity is amember of are stored in the corresponding entity profile (e.g., inmember data 212 and/or provider data 213).

In various embodiments, a cluster vector may be determined for one ormore clusters. For instance, provider specialty cluster may compriseentity vectors for a plurality of a providers. The entity vectors forthe plurality of providers may be aggregated to generate a clustervector for the provider specialty cluster. Cluster vectors for variousidentified clusters may be similarly determined and/or generated byaggregating the entity vectors of the entities of the clusters.

In various embodiments, one or more signal values may be determined,calculated, and/or the like. An example signal value determined,calculated, and/or the like in an example embodiment is a providerbehavior distance from peer cluster. In various embodiments, theprovider behavior distance from peer cluster is the distance between anentity vector corresponding to the provider and the cluster vector ofthe peer cluster corresponding to and/or associated with the provider.Another example signal value determined, calculated, and/or the like inan example embodiment is a provider behavior distance from specialtycluster. In various embodiments, the provider behavior distance fromspecialty cluster is the distance between an entity vector correspondingto the provider and the cluster vector of the provider specialty clustercorresponding to and/or associated with the provider. Another examplesignal value determined, calculated, and/or the like in an exampleembodiment is a member-provider signal value corresponding to the memberbehavior distance from provider. In various embodiments, the memberbehavior distance from provider is the distance between an entity vectorcorresponding to the member and the entity vector corresponding to aprovider associated with the member (e.g., a provider corresponding to aprovider identifier of an instance of claim information/data that alsoincludes the member identifier corresponding to the member/patient).Another example signal value determined, calculated, and/or the like inan example embodiment is a provider behavior distance from specialtycluster. In various embodiments, the provider behavior distance fromspecialty cluster is the distance between an entity vector correspondingto the provider and the cluster vector of the provider specialty clustercorresponding to and/or associated with the provider. In an exampleembodiment, another signal value that may be determined is the distancebetween an entity vector corresponding to a provider and a particulartreatment (e.g., the vector embedding of a medical code corresponding tothe treatment). Another example signal value determined, calculated,and/or the like in an example embodiment, is the distance between anentity vector corresponding to a provider and a member cluster vector.

In various embodiments, one or more signal values that correspond tochanges in time may be determined. For example, a provider profilecorresponding to a provider and stored in the provider data 213 mayinclude entity vectors corresponding to the provider and correspondingto earlier/previous time periods. Similarly, a member profilecorresponding to a member/patient and stored in the member data 212 mayinclude entity vectors corresponding to the member and corresponding toearlier/previous time periods. In various embodiments, the one or moresignal values comprise at least one signal value that is the distancebetween an entity or cluster vector corresponding to a first time periodand an entity or cluster vector corresponding to a previous time period(e.g., immediately preceding time period and/or the like). For instance,a change of provider behavior with time signal value may be determined.In an example embodiment, the change of provider behavior with timesignal value is the distance between (a) the entity vector correspondingto the provider and corresponding to the first time period and (b) theentity vector corresponding to the provider and corresponding to theprevious time period (e.g., the immediately preceding time period). Forexample, a change in peer cluster behavior with time signal value may bedetermined. In an example embodiment, the change of peer clusterbehavior with time signal value is the distance between (a) the clustervector corresponding to the provider peer cluster and corresponding tothe first time period and (b) the cluster vector corresponding to theprovider peer cluster and corresponding to the previous time period(e.g., the immediately preceding time period). For instance, a change inspecialty cluster behavior with time signal value may be determined. Inan example embodiment, the change of specialty cluster behavior withtime signal value is the distance between (a) the cluster vectorcorresponding to the provider specialty cluster and corresponding to thefirst time period and (b) the cluster vector corresponding to theprovider specialty cluster and corresponding to the previous time period(e.g., the immediately preceding time period). For example, a change ofmember behavior with time signal value may be determined. In an exampleembodiment, the change of member behavior with time signal value is thedistance between (a) the entity vector corresponding to themember/patient and corresponding to the first time period and (b) theentity vector corresponding to the member/patient and corresponding tothe previous time period (e.g., the immediately preceding time period).

In various embodiments, a variety of distance measurements may be usedto determine, generate, and/or calculate the signal values, such asEuclidean distance, cosine distance, and/or other distance measurement.

These signal values and corresponding metadata (e.g., provideridentifier, member identifier, time period, cluster identifierconfigured to identify the corresponding peer cluster or specialtycluster, and/or the like as appropriate for the signal value) may bestored (e.g., in the corresponding member and/or provider profilesand/or in a separate behavior signal data store). A behavior signalcomprises a time ordered series of corresponding signal values. Forinstance, the provider behavior signal comprises a provider behaviorsignal value corresponding to a time tl, a provider behavior signalvalue corresponding to a time t2, and/or the like with each of theprovider behavior signal values ordered in chronological order of thecorresponding times.

The behavior signals may be analyzed to determine patterns in providerbehavior for an individual provider, patterns in behavior of clusters ofproviders (e.g., peer clusters, specialty clusters, and/or the like),pattern in behavior of particular members, and/or the like. In variousembodiments, these behavior signals may be monitored to identify changesin entity and/or entity cluster behaviors. In various embodiments, thebehavior signals and patterns identified therefrom may be used toidentify entities exhibiting anomalous behavior (e.g., providers and/ormembers/patients exhibiting anomalous behavior), in various embodiments.In various embodiments, the behavior patterns may be used to determinesuggestions for changes in behavior that may improve an entity'sbehavior. For example, a first provider may routinely submit claimsincluding a particular medical code and other providers within a peercluster or specialty cluster that comprises the first provider mayroutinely submit claims including a different, but similar medical code.Thus, the first provider's behavior may appear anomalous due to thisdifferent but similar medical code usage. A suggestion may then bedetermined, generated, and/or the like (and provided to the firstprovider) that the first provider should use the different but similarmedical code rather than the particular medical code, as appropriate. Inanother example, the behavior signals may be used to generatepredictions of entity and/or entity cluster behaviors responsive tovarious external factors (e.g., insurance company and/or governmentpolicy changes, provider ratings, pricing changes, insurance planchanges, news, events, and/or other happenings that are not governedand/or controlled by a particular entity (e.g., provider ormember/patient)).

FIG. 4 provides a flowchart illustrating various processes, procedures,operations, and/or the like that may be performed (e.g., by an analysiscomputing entity 65) to generate an entity vector corresponding to anentity (e.g., member/patient or provider) and corresponding to a timeperiod, according to an example embodiment. Starting at step/operation402, instances of claims information/data are received. For instance,user computing entities 30 (e.g., provider computing entities and/ormember computing entities) may be submit claims that are received by aclaims computing entity 50. The claims computing entity 50 may provideinstances of claim information/data corresponding to the claims suchthat the analysis computing entity 65 receives the instances of claiminformation/data. For example, the analysis computing entity 65 maycomprise means, such as processing element 205, memory (e.g., volatilememory 207 and/or non-volatile memory 206), communications interface208, and/or the like, configured for receiving instances of claiminformation/data. As noted above, an instance of claim information/datamay comprise a plurality of elements such as entity identifyinginformation/data, categorical elements, numerical elements, temporalinformation/data, and/or the like. In various embodiments, the instancesof claim information/data correspond to a time period. For instance, thetemporal information/data of the instances of claim information/dataindicate that the service(s) and/or product(s) corresponding to theinstance of claim information/data was performed and/or provided duringthe time period. In an example embodiment, the time period maycorrespond to a day, a week, a two week period, a month, a quarter(e.g., a three month period), a year, and/or the like.

At step/operation 404, the elements of the instances of claiminformation/data are extracted. For example, the analysis computingentity 65 may extract elements of the instances of claiminformation/data. For instance, the analysis computing entity 65 maycomprise means, such as processing element 205, memory (e.g., volatilememory 207 and/or non-volatile memory 206), and/or the like forextracting elements of the instances of claim information/data. Forexample, categorical and/or numerical elements of the instances of claiminformation/data may be extracted from the instances of claiminformation/data. In an example embodiment, each extracted element isassociated with metadata that includes a claim identifier identifyingthe corresponding claim, a member identifier extracted from thecorresponding claim, a provider identifier extracted from thecorresponding claim, temporal information/data extracted from thecorresponding claim, and/or the like. In an example embodiment,extracting the elements from an instance of claim information/data mayinclude transforming an instance of claim information/data into aparticular format comprising an array of categorical elements and anycorresponding numerical elements.

At step/operation 406, the extracted elements of the instances of claiminformation/data are transformed into element vectors. In an exampleembodiment, element vectors are generated based at least in part on theextracted elements of the instances of claim information/data. Forinstance, an embedding module 220 operating on the analysis computingentity 65 may determine, generate, and/or the like, an element vectorfor each of the extracted elements. For example, the analysis computingentity 65 may comprise means, such as processing element 205, memory(e.g., volatile memory 207 and/or non-volatile memory 206), and/or thelike for determining, generating, and/or the like element vectors basedat least in part on the extracted elements of the instances of claiminformation/data. In various embodiments, the metadata associated withan extracted element is associated with the corresponding elementvector. For instance, the claim identifier, any entity identifier, anytemporal information/data and/or the like associated with an extractedelement may be associated with the corresponding element vector.

In various embodiments, the embedding module 220 is amachine-learning-based model. For example, the embedding module 220 maybe a classifier and/or other model configured to generate vectorscorresponding to categorical elements of instances of claiminformation/data such that the vectors encode relationships between thecategorical elements. For instance, a first categorical element may be amedical code corresponding to a right arm break, a second categoricalelement may be a medical code corresponding to a left arm break, and athird categorical element may be a medical code corresponding to strepthroat. The embedding module 220 may generate a first vectorcorresponding to the first categorical element, a second vectorcorresponding to the second categorical element, and a third vectorcorresponding to the third categorical element. The first vector andsecond vector may be close to one another (e.g., have a relatively smalldistance therebetween) and the third vector may be a larger distancefrom the first and second vectors, thus indicating that a right armbreak and a left arm break are more similar to one another than either aright arm break or a left arm break is to strep throat. In an exampleembodiment, the embedding module 220 may use a library that waspreviously generated (e.g., based at least in part on training of amachine-learning-based model on instances of claim information/dataand/or other medical information/data for a previous time period) toassign, generate, and/or determine element vectors for the extractedelements.

At step/operation 408, claim vectors may be generated. For example, theanalysis computing entity 65 may generate claim vectors from the elementvectors. For instance, the analysis computing entity 65 may comprisemeans, such as processing element 205, memory (e.g., volatile memory 207and/or non-volatile memory 206), and/or the like for generating a claimvector from a plurality of element vectors. For example, the elementvectors may be sorted by the associated metadata. For instance, allelement vectors associated with a first claim identifier may beaggregated together to generate a first claim vector corresponding tothe first claim identifier and all of the element vectors associatedwith a second claim identifier may be aggregated together to generate asecond claim vector corresponding to the second claim identifier. Forexample, FIG. 5A illustrates an example plurality of element vectors 502(e.g., 502A, 502B, shown as dashed lines). The plurality of elementvectors 502 are aggregated to generate the corresponding claim vector504 (shown as the thick solid line). In an example embodiment,aggregating the element vectors 502 to generate the corresponding claimvector 504 comprises performing a vector addition of the element vectors502. In an example embodiment, aggregating the element vectors 502 togenerate the corresponding claim vector 504 comprises performing anaverage of the element vectors 502. In an example embodiment, theelement vectors 502 are aggregated by the embedding module 220 and/or aportion thereof (e.g., a machine learning trained auto-encoder networkand/or the like) to generate the corresponding claim vector 504. Invarious embodiments, a variety of methods may be used to aggregate theelement vectors 502 associated with a common claim identifier togenerate a claim vector associated with the claim identifier.

Continuing with FIG. 4, at optional step/operation 410, an anomaly score(e.g., reconstruction error), infused semantics score, and/or the likefor an instance of claim information/data are determined. For instance,one or more element vectors corresponding to a claim vector and/or theclaim vector may be provided to a trained auto-encoder network. Theauto-encoder network may determine an anomaly score (e.g.,reconstruction error), infused semantics score, and/or the like based atleast in part on the one or more element vectors and/or claim vector. Inan example embodiment, the auto-encoder network may be part of theembedding module 220. For example, the analysis computing entity 65 maycomprise means, such as processing element 205, memory (e.g., volatilememory 207 and/or non-volatile memory 206), and/or the like, fordetermining an anomaly score, infused semantics score, and/or the likefor an instance of claim information/data.

At step/operation 412, the claim vectors are grouped based at least inpart on entity identifiers associated therewith and aggregated togenerate entity vectors. For instance, a claim vector may be associatedwith metadata that includes a claim identifier, member identifier,provider identifier, and/or the like. The claim vectors corresponding tothe time period may be grouped such that all of the claim vectorsassociated with a first provider identifier are aggregated to generatean entity vector corresponding to the first provider (e.g., identifiedby the first provider identifier) and corresponding to the time period.In various embodiments, the claim vectors may be grouped by memberidentifier to generate entity vectors each corresponding to a member. Invarious embodiments, the claim vectors may be grouped by provideridentifier to generate entity vectors each corresponding to a provider.For example, claim vectors associated with a common provider identifiermay be aggregated to generate an entity vector corresponding to theprovider identified by the common provider identifier. For instance,FIG. 5B illustrates an example plurality of claim vectors 504 (e.g.,504A, 504B, shown as thick solid lines). The plurality of claim vectors504 are aggregated to generate the corresponding entity vector 506(shown as the thick dotted line). In an example embodiment, aggregatingthe claim vectors 504 to generate the corresponding entity vector 506comprises performing a vector addition of the claim vectors 504. In anexample embodiment, aggregating the claim vectors 504 to generate thecorresponding entity vector 504 comprises performing an average of theclaim vectors 504. In various embodiments, a variety of methods may beused to aggregate the claim vectors 504 associated with a common memberor provider identifier to generate an entity vector 506 corresponding tothe member or provider. In various embodiments, the entity vectors arestored in corresponding entity profiles (e.g., member profiles stored inmember data 212, provider profiles stored in provider data 213) inassociation with time information/data indicating the time period towhich the entity vector corresponds.

In various embodiments, the entity vectors corresponding to a timeperiod may be used to determine behavior signal values corresponding tothe time period. Time ordered series of these behavior signal valuesprovide behavior signals that may be monitored and/or analyzed todetermine patterns of entity behavior. In various embodiments, clustersof providers and/or members may be identified. For example, geographicclusters corresponding to providers that practice in the same geographicarea (e.g., country, region of a country, state, region of a state,county, city, and/or the like) may be identified based at least in parton geographic locations associated with the providers and acorresponding geographic cluster identifier may be stored in associationwith a provider profile that identifies one or more geographic clustersof which the provider is a part. In another example, providers may beclustered based at least in part on specialties. For instance, theproviders may be clustered based at least in part on proclaimedspecialties (e.g., association with a professional specialtyorganization, registration of the provider as a practicing a particularspecialty, and/or the like). For example, the provider peer clusters maybe identified using a clustering algorithm to identify clusters ofentity vectors corresponding to providers. In various embodiments,member geographic clusters and/or member peer clusters may beidentified. In various embodiments, the behavior signals includebehavior signals that indicate how a provider's behavior changes withrespect to a corresponding cluster, how the behavior of a clusterchanges over time, how the behavior of a provider changes over time, howthe behavior of a member changes with respect to one or more providers,how the behavior of a member changes over time, and/or the like.

FIG. 6 provides a flowchart illustrating various processes, procedures,operations, and/or the like performed (e.g., by an analysis computingentity 65) to generate behavior signals and provide the behavior signalsand/or results of analyzing the behavior signals. Starting atstep/operation 602, cluster vectors corresponding to a time period aredetermined. For instance, the analysis computing entity 65 maydetermine, generate, calculate, and/or the like cluster vectorscorresponding to the time period. For example, the analysis computingentity 64 may comprise means, such as processing element 205, memory(e.g., volatile memory 207 and/or non-volatile memory 206), and/or thelike, for determining, generating, calculating, and/or the like clustervectors corresponding to the time period. For instance, a clusteringmodule 222 may be executed by the analysis computing entity 65 toidentify, generate, and/or the like one or more provider geographicalclusters, member geographical clusters, provider specialty clusters,provider peer clusters, member peer clusters, and/or the like.

In various embodiments, an entity profile corresponding to an entity(e.g., provider or member/patient) is associated with one or morecluster identifiers (e.g., corresponding to geographic clusters,specialty clusters, peer clusters, and/or the like). In some exampleembodiments, an entity's inclusion in a cluster is static with respectto time (e.g., unchanging) unless a change is made to the entity profile(e.g., a provider is practicing in a new location, a member has moved toa new location, a provider is practicing a different specialty, and/orthe like). For example, a clustering algorithm may be used to generateprovider peer clusters during one time period and the providers may beassumed to maintain their association with the provider peer clusterthrough succeeding time periods. In some example embodiments, providerpeer clusters (and/or member peer clusters) are determined for each timeperiod (e.g., prior to determining behavior signal values correspondingto the peer clusters for that time period). For instance, as shown inFIG. 7, a plurality of entity vectors 506 (e.g., 506A, 506B; eachcorresponding to a provider) may be determined (e.g., as described abovewith respect to FIG. 4, in various embodiments). A clustering algorithmmay be used to identify clusters 702 (e.g., 702A, 702B, 702C) from theplurality of entity vectors 506. In an example embodiment, adensity-based clustering algorithm is used to identify clusters 702 ofentity vectors 506. As should be understood, each entity vector 506 isassociated with an entity identifier (e.g., provider identifier ormember identifier) that identifies the corresponding entity (e.g.,provider or member/patient). Thus, the providers and/or members/patientsmay each be associated with a peer cluster and the corresponding entityprofile may be associated with a peer cluster identifier configured toidentify the corresponding peer cluster.

A cluster vector 704 (e.g., 704A, 704B) may be generated by aggregatingthe entity vectors 506 of the entities in the cluster. For example, afirst cluster vector 704A may be generated by aggregating the entityvectors 506 of each of the entities in the first cluster. For instance,each of the entity vectors 506 contained within the dotted line thatdefines the boundary of the first cluster vector 702A may be aggregatedto generate the first cluster vector 704A. In an example embodiment,aggregating the entity vectors 506 to generate the corresponding clustervector 704 comprises performing a vector addition of the entity vectors506. In an example embodiment, aggregating the entity vectors 506 togenerate the corresponding cluster vector 704 comprises performing anaverage of the entity vectors 506. In various embodiments, a variety ofmethods may be used to aggregate the entity vectors 506 associated witha common cluster identifier to generate a cluster vector 704corresponding to the cluster. In various embodiments, the clustervectors and associated cluster identifiers are stored (e.g., innon-volatile memory 206) in association with time information/dataindicating the time period to which the cluster vector 704 corresponds.

Continuing with FIG. 6, at step/operation 604, behavior signal valuescorresponding to the time period are determined. For example, thebehavior signal values may indicate a relationship between an entity anda cluster and/or two entities at a time period. For instance, thebehavior signal values may indicate a change in behavior of an entityover time, change in behavior of a cluster over time, and/or the likecorresponding to the time period. For example, the analysis computingentity 65 may determine one or more behavior signal values correspondingto the time period. For instance, the analysis computing entity 65 maycomprise means, such as processing element 205, memory (e.g., volatilememory 207 and/or non-volatile memory 206), and/or the like, fordetermining one or more behavior signal values corresponding to the timeperiod. For example, a signal generation module 224 executing on theanalysis computing entity 65 may generate one or more behavior signalvalues.

For instance, FIG. 8A illustrates a cluster 800 having cluster vector804 and comprising entity vector 802 at time T=t1 and the same cluster800 having cluster vector 804 and comprising entity vector 804 at timeT=t2, where t1 and t2 indicate different time periods. In an exampleembodiment, t1 and t2 are immediately adjacent time periods such thattime tl immediately precedes time t2. A provider behavior distance fromcluster (e.g., geographic cluster, specialty cluster, and/or peercluster) may be determined. For example, at time T=t1, the providerbehavior distance from cluster is shown by entity-cluster distance 806Aand at time T=t2, the provider behavior distance from cluster is shownby entity-cluster distance 806B. In some embodiments, a member-providerbehavior signal value (e.g., member behavior distance from an associatedprovider) may be determined. For instance, the distance between anentity vector corresponding to a member/patient and an entity vectorcorresponding to a provider (e.g., where a member identifiercorresponding to the member/patient and a provider identifiercorresponding to the provider are both present in an instance of claiminformation/data during the time period) may be determined. Somebehavior signal values correspond to changes in entity and/or clusterbehavior over time (e.g., between time periods, between immediatelyadjacent time periods, and/or the like). For example, an entity behaviorchange in time signal value may be determined by determining the scalarand/or vector difference between the vector 808B corresponding to entityvector 802 at time T=t2 and vector 808A corresponding to the entityvector 802 at time T=t1.

For instance, a provider behavior change in time signal value may bedetermined for one or more providers. For example, a member behaviorchange in time signal value may be determined for one or moremembers/patients. For instance, a cluster behavior change in time signalvalue may be determined by determining the scalar and/or vectordifference between the vector 810B corresponding to cluster vector 804at time T=t2 and vector 810A corresponding to the cluster vector 804 attime T=t1. For example, a provider geographical, specialty, and/or peercluster behavior change in time signal value may be determined for oneor more provider geographical, specialty, and/or peer clusters. Forinstance, FIG. 8B illustrates an example provider behavior signal 850that illustrates how an example provider's behavior change over time.

Returning to FIG. 6, at step/operation 606, one or more entity profiles(e.g., member and/or provider profiles stored in member data 212 andprovider data 213, respectively) and/or cluster profiles may be updatedto include the corresponding determined behavior signal values. Invarious embodiments, the analysis computing entity 65 updates one ormore entity profiles (e.g., member and/or provider profiles stored inmember data 212 and provider data 213, respectively) and/or clusterprofiles to include the corresponding determined behavior signal values.For example, the analysis computing entity 65 may comprise means, suchas processing element 205, memory (e.g., volatile memory 207 and/ornon-volatile memory 206), and/or the like, for updating one or moreentity profiles and/or one or more cluster profiles. For instance, anentity profile and/or a cluster profile may include one or more behaviorsignal arrays, where each component and/or position of the array is abehavior signal value corresponding to a particular time period. Thedetermined behavior signal values may be added to the correspondingbehavior signal arrays in a position corresponding to the time periodthat corresponds to the instances of claim information/data processed togenerate the behavior signal values.

At step/operation 608, one or more behavior signals corresponding to oneor more entities and/or one or more clusters may be analyzed to identifybehavior patterns, determine suggestions for provider improvement,identify anomalous behavior patterns, determine future behaviorpredictions, and/or the like. For example, one or more behavior signalsmay be provided as input to signal processing module 226 (e.g.,executing on the analysis computing entity 65) configured to analyze oneor more behavior signals to identify behavior patterns, determinesuggestions for provider improvement, identify anomalous behaviorpatterns, determine future behavior predictions, and/or the like. Forinstance, the analysis computing entity 65 may analyze one or morebehavior signals corresponding to one or more entities and/or one ormore clusters to identify behavior patterns, determine suggestions forprovider improvement, identify anomalous behavior patterns, determinefuture behavior predictions, and/or the like. For example, the analysiscomputing entity 65 may comprise means, such as processing element 205,memory (e.g., volatile memory 207 and/or non-volatile memory 206),and/or the like for identifying behavior patterns, determiningsuggestions for provider improvement, identifying anomalous behaviorpatterns, determining future behavior predictions, and/or the like. Inan example embodiment, one or more providers may be identified within aprovider cluster (e.g., geographic cluster, specialty cluster, or peercluster) whose behavior influences and/or leads the evolution ofbehavior of the corresponding provider cluster.

For instance, one or more behavior signals and/or externalinformation/data (e.g., information/data corresponding to externalfactors such as insurance company and/or government policy changes,provider ratings, pricing changes, insurance plan changes, news, events,and/or other happenings that are not governed and/or controlled by aparticular entity (e.g., provider or member/patient)) to amachine-learning-based model. For example, the one or more behaviorsignals and/or external information/data may be provided to a neuralnetwork and/or the like trained to identify behavior patterns within abehavior signal, correlations between features of a behavior pattern andfeatures of external information/data, and/or the like. In an exampleembodiment, the external information/data provided may includeprediction external information/data corresponding to one or more timeperiods that have not yet occurred and one or more predicted behaviorsignal values may be generated (e.g., corresponding to the one or moretime periods that have not yet occurred) based at least in part on thedetermined and/or identified behavior patterns, correlations, and/or thelike.

In an example embodiment, the analysis computing entity 65 may analyzeone or more behavior signals, cluster vectors, entity vectors, claimsvectors, and/or element vectors corresponding to the time period toidentify suggestions that may improve a provider's performance and/orbring an entity vector corresponding to the provider into closerproximity with the corresponding cluster vector. For instance, a firstprovider may use a first medical code when submitting a claim. Otherproviders within the same provider peer cluster may use a second medicalcode, which is similar to the first medical code (e.g., corresponds to asimilar diagnosis, procedure, medication, equipment, and/or the like).Thus, the entity vector corresponding to the first provider may be movedcloser to the cluster vector of the corresponding provider peer clusterif the first provider used the second medical code instead of the firstmedical code. It may therefore be determined that the first provider'sperformance would be improved if the first provider switched to usingthe second medical code (and the corresponding diagnosis, procedure,medication, equipment, and/or the like) as appropriate within the firstprovider's practice.

In an example embodiment, anomalous behavior patterns may be identified.For example, the one or more behavior signals may be processed (e.g., bya machine-learning-based model and/or the like executing on the analysiscomputing entity 65) to identify anomalous behavior patterns. Forinstance, significant changes in provider's behavior and/or significantchange in a provider's behavior with respect to provider geographicalcluster, specialty cluster, and/or peer cluster with which the provideris associated may be flagged as anomalous behavior. Similarly, if amember-provider behavior signal for a first member and a first provideris significantly different from other member-provider behavior signalsfor the first provider, it may be determined that the first member isexhibiting anomalous behavior. In various embodiments, a variety oftechniques may be used to identify provider and/or member behaviorpatterns that are indicative of anomalous behavior.

At step/operation 610, one or more behavior signals and/or results ofanalyzing one or more behavior signals provided. For example, theanalysis computing entity 65 may provide (e.g., transmit) one or morebehavior signals and/or results of analyzing one or more behaviorsignals such that a user computing entity 30 (e.g., insurance companyaffiliate computing entity, provider computing entity) may receive theone or more behavior signals and/or results of analyzing one or morebehavior signals. In various embodiments, the user computing entity 30may be configured to provide (e.g., via a user interface thereof) aninteractive user interface. For instance, the interactive user interfacemay be provided via an online portal, dashboard provided via a webbrowser, via a dedicated application, and/or the like. The interactiveuser interface may display and/or otherwise provide the one or morebehavior signals and/or results of analyzing one or more behaviorsignals.

FIG. 9 illustrates an example dashboard 900 (e.g., displayed via adisplay 316 of a user computing entity 30). In various embodiments, thedashboard 900 provides a representation of at least some of theinstances of claim information/data that was used to generate thebehavior signals and the behavior signals and/or result(s) of analyzingbehavior signals. In an example embodiment, the behavior signals trackthe pairwise position over time between entity vectors corresponding toproviders and/or members/patients and/or between entity vectorscorresponding to providers and/or members/patients and cluster vectorsfor corresponding clusters.

In an example embodiment, the dashboard 900 comprises a claims datasummary 902. For example, the claims data summary 902 may provide asummary of a group of instances of claim information/data that wasanalyzed to provide the behavior signals. For instance, the claims datasummary 902 may provide a summary of the instances of claiminformation/data corresponding to the current and/or most recent timeperiod. For example, the claims data summary 902 may indicate the numberof providers and/or number of members represented in the instances ofclaim information/data, the number of claims represented by theinstances of claim information/data, amount paid responsive toprocessing the claims represented by the instances of claiminformation/data, and/or the like. In an example embodiment, thedashboard 900 may comprise a provider peer cluster behavior distancesection 904 that illustrates the relative size of provider peer clustersand the amount paid to each provider peer cluster. In an exampleembodiment, the dashboard 900 may comprise a behavior trend section 906that may illustrate one or more trends identified in various behaviorsignals. In an example embodiment, the dashboard 900 may comprise aprovider behavior distance with respect to peer cluster section 908 thatillustrates the distance an entity vector corresponding to a provider isfrom the cluster vector of a corresponding cluster. In an exampleembodiment, the provider behavior distance with respect to peer clustersection 908 illustrates one or more medical codes used by one or moreproviders and suggestions for codes that the one or more providers couldswitch to to cause the provider's behavior distance with respect to thepeer cluster to be reduced. In an example embodiment, the dashboard 900may comprise a selected provider/cluster indicator 910. In an exampleembodiment, various aspects of the dashboard 900 may be broken down bygeographic location. Various components of the illustrated dashboard 900may be swapped with various other graphical representations of variousbehavior signals and/or results of analyzing the behavior signals, invarious embodiments.

FIG. 10 illustrates a prediction dashboard view 1000 of the dashboard900 displayed as an interactive user interface via the user interface ofa user computing entity 30. For instance, a model (e.g.,machine-learning-based model) may be used to model a behavior signal.Based at least in part on external information/data (e.g.,information/data corresponding to external factors such as insurancecompany and/or government policy changes, provider ratings, pricingchanges, insurance plan changes, news, events, and/or other happeningsthat are not governed and/or controlled by a particular entity (e.g.,provider or member/patient)) provided to the model. Based at least inpart on the modeled behavior signal and the external information/data,the model may predict future signal values for the behavior signal. Thepredicted future signal values may be provided through the predictiondashboard view 1000.

In various embodiments, a variety of other dashboard views may beavailable for user review. For example, the dashboard may be configuredto provide various behavior signals and/or results of analyzing thebehavior signals via an interactive user interface provided via the userinterface of a user computing entity 30.

V. Conclusion

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A method for tracking entity behavior over time, the methodcomprising: receiving, by an analysis computing entity, an instance ofclaim data, wherein the instance of claim data (a) corresponds to aparticular entity, and (b) comprises a plurality of features;generating, by the analysis computing entity and one or more machinelearning models, a claim vector for the instance of claim data based atleast in part on the plurality of features for the instance of claimdata; adding, by the analysis computing entity, the claim vector to aplurality of claim vectors, wherein each claim vector of the pluralityof claim vectors corresponds to the particular entity; generating, bythe analysis computing entity and based at least in part on theplurality of claim vectors, an entity vector corresponding to theparticular entity; based at least in part on the entity vector and atleast one of (a) an entity profile corresponding to the particularentity, or (b) an entity cluster with which the particular entity isassociated, determining, by the analysis computing entity, a behaviorsignal value for the particular entity; updating, by the analysiscomputing entity, a behavior signal to include the behavior signalvalue, wherein the behavior signal (a) is stored in a data structure,and (b) comprises at least two behavior signal values; and providing, bythe analysis computing entity, the behavior signal for at least one of(a) display via an interactive user interface of a user computing entityor (b) further processing.
 2. The method of claim 1, wherein theparticular entity is a healthcare provider or a member of an insurancepolicy.
 3. The method of claim 1, wherein generating the claim vectorcomprises generating element vectors corresponding to elements of theplurality of features for the instance of claim data.
 4. The method ofclaim 1, wherein the entity cluster is identified using density-basedclustering analysis of a plurality of entity vectors.
 5. The method ofclaim 1, wherein the entity cluster is one of a provider geographiccluster, a provider specialty cluster, or a provider peer cluster. 6.The method of claim 1, wherein the further processing comprisesdetection of anomalous behavior.
 7. The method of claim 1, wherein thebehavior prediction is performed based at least in part on externalinformation corresponding to external factors.
 8. The method of claim 1,wherein the behavior signal value is (a) a provider behavior distancefrom provider peer cluster, (b) a provider behavior distance fromprovider specialty cluster, (c) a member behavior distance fromprovider, (d) a provider behavior distance from a treatment, (e) achange of provider behavior with time, (f) a change in peer clusterbehavior with time, (g) a change in specialty cluster behavior withtime, or (h) a change of member behavior with time.
 9. An apparatuscomprising at least one processor, at least one communicationsinterface, and at least one memory including computer program code, thecomputer program code comprising executable instructions, the at leastone memory and computer program code configured to, with the processor,cause the apparatus to at least: receive an instance of claim data,wherein the instance of claim data (a) corresponds to a particularentity, and (b) comprises a plurality of features; generate, by one ormore machine learning models, a claim vector for the instance of claimdata based at least in part on the plurality of features for theinstance of claim data; add the claim vector to a plurality of claimvectors, wherein each claim vector of the plurality of claim vectorscorresponds to the particular entity; generate, based at least in parton the plurality of claim vectors, an entity vector corresponding to theparticular entity; based at least in part on the entity vector and atleast one of (a) an entity profile corresponding to the particularentity, or (b) an entity cluster with which the particular entity isassociated, determine a behavior signal value for the particular entity;update a behavior signal to include the behavior signal value, whereinthe behavior signal (a) is stored in a data structure, and (b) comprisesat least two behavior signal values; and provide the behavior signal forat least one of (a) display via an interactive user interface of a usercomputing entity or (b) further processing.
 10. The apparatus of claim9, wherein the particular entity is a healthcare provider or a member ofan insurance policy.
 11. The apparatus of claim 9, wherein generatingthe claim vector comprises generating element vectors corresponding toelements of the plurality of features for the instance of claim data.12. The apparatus of claim 9, wherein the entity cluster is identifiedusing density-based clustering analysis of a plurality of entityvectors.
 13. The apparatus of claim 9, wherein the entity cluster is oneof a provider geographic cluster, a provider specialty cluster, or aprovider peer cluster.
 14. The apparatus of claim 9, wherein the furtherprocessing comprises detection of anomalous behavior.
 15. The apparatusof claim 9, wherein the behavior prediction is performed based at leastin part on external information corresponding to external factors. 16.The apparatus of claim 9, wherein the behavior signal value is (a) aprovider behavior distance from provider peer cluster, (b) a providerbehavior distance from provider specialty cluster, (c) a member behaviordistance from provider, (d) a provider behavior distance from atreatment, (e) a change of provider behavior with time, (f) a change inpeer cluster behavior with time, (g) a change in specialty clusterbehavior with time, or (h) a change of member behavior with time.
 17. Acomputer program product comprising at least one non-transitorycomputer-readable storage medium having computer-executable program codeportions stored therein, the computer-executable program code portionscomprising program code instructions, the computer program codeinstructions, when executed by a processor of a computing entity, areconfigured to cause the computing entity to: receive an instance ofclaim data, wherein the instance of claim data (a) corresponds to aparticular entity, and (b) comprises a plurality of features; generate,by one or more machine learning models, a claim vector for the instanceof claim data based at least in part on the plurality of features forthe instance of claim data; add the claim vector to a plurality of claimvectors, wherein each claim vector of the plurality of claim vectorscorresponds to the particular entity; generate, based at least in parton the plurality of claim vectors, an entity vector corresponding to theparticular entity; based at least in part on the entity vector and atleast one of (a) an entity profile corresponding to the particularentity, or (b) an entity cluster with which the particular entity isassociated, determine a behavior signal value for the particular entity;update a behavior signal to include the behavior signal value, whereinthe behavior signal (a) is stored in a data structure, and (b) comprisesat least two behavior signal values; and provide the behavior signal forat least one of (a) display via an interactive user interface of a usercomputing entity or (b) further processing.
 18. The computer programproduct of claim 17, wherein the behavior signal value is (a) a providerbehavior distance from provider peer cluster, (b) a provider behaviordistance from provider specialty cluster, (c) a member behavior distancefrom provider, (d) a provider behavior distance from a treatment, (e) achange of provider behavior with time, (f) a change in peer clusterbehavior with time, (g) a change in specialty cluster behavior withtime, or (h) a change of member behavior with time.